arxiv: 2605.14001 · v1 · submitted 2026-05-13 · 🪐 quant-ph

Recognition: no theorem link

QUACOD: Quantum Optimization via Coordinate Descent for Scalable Drone Scheduling

Van-Quang-Huy Nguyen , Hoang-Quan Nguyen , Samee U. Khan , Ilya Safro , Khoa Luu

Authors on Pith no claims yet

Pith reviewed 2026-05-15 05:36 UTC · model grok-4.3

classification 🪐 quant-ph

keywords quantum optimizationcoordinate descentdrone schedulingNISQscalabilityvehicle routingcombinatorial optimization

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The pith

QUACOD decomposes drone scheduling into quantum-solvable subproblems to scale five times larger than direct methods on limited qubits.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents QUACOD as a way to tackle drone route optimization when the full problem exceeds available quantum hardware resources. It applies coordinate descent to split the original high-complexity scheduling task into smaller subproblems that each fit on current NISQ devices. Experiments report shorter total completion times than prior quantum schedulers plus the ability to handle five times more drones and thirty-five times more routes. A sympathetic reader would care because this decomposition keeps quantum optimization usable for logistics without requiring larger machines. The work also shows that hardware-efficient circuits can handle the resulting subproblems effectively.

Core claim

QUACOD decomposes the drone scheduling optimization problem via coordinate descent into multiple subproblems that are solved independently using quantum optimization routines, producing schedules with better completion times and supporting instances with up to five times more drones and thirty-five times more routes than the previous state-of-the-art quantum approach while operating under qubit limits.

What carries the argument

Coordinate descent decomposition, which reduces the full high-dimensional drone scheduling objective into lower-dimensional subproblems solved sequentially or in parallel on quantum hardware.

If this is right

Drone fleets up to five times larger become schedulable on existing quantum hardware without increasing qubit count.
Hardware-efficient ansatze become sufficient for practical optimization once the problem is decomposed.
Similar coordinate-descent splits may extend the reach of quantum methods to other vehicle-routing and logistics tasks.
The approach reduces dependence on future fault-tolerant quantum computers for near-term combinatorial problems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same splitting strategy could apply to classical hybrid solvers for even larger instances before quantum hardware is invoked.
If subproblem independence holds across domains, the method offers a template for scaling other quantum combinatorial optimizers.
Real-world drone telemetry data would provide a direct test of whether the reported scalability gains survive operational noise and constraints.
The technique suggests a general pattern for making quantum optimization practical by trading one global solve for many local ones.

Load-bearing premise

That independently solved subproblems can be recombined into a schedule whose quality matches or exceeds what solving the entire problem at once would achieve.

What would settle it

A head-to-head run on an instance size where the prior direct quantum solver still fits, measuring whether QUACOD's final completion time is worse than the direct solver's output.

Figures

Figures reproduced from arXiv: 2605.14001 by Hoang-Quan Nguyen, Ilya Safro, Khoa Luu, Samee U. Khan, Van-Quang-Huy Nguyen.

**Figure 2.** Figure 2: The hybrid quantum-classical optimization loop of a variational [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Heuristic for transformation from Cmax minimization to convergence toward T /q. (a)-(c) Route assignment minimizes Cmax toward the optimal solution. (d)-(e) Minimizing the sum of squared distances P j∈[q] Tj − T q 2 instead of Cmax converges toward the same optimal solution. the sum of squared distances between each Tj and T q . The objective function in Eqn. (8) can thus be rewritten as: f ({xi,j}) = … view at source ↗

**Figure 4.** Figure 4: A single-layer hardware-efficient ansatz for an [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Convergence process of QUACOD on the Augerat dataset [29] adapted for the drone delivery problems [30]. Column (A) shows the objective function [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

read the original abstract

Quantum computing has demonstrated its potential to solve various optimization problems, including drone scheduling, which is important not only for drone delivery but also for logistics in general. However, one of the main obstacles is that practical drone scheduling settings typically require quantum resources that current hardware cannot provide. Therefore, in this work, we introduce a new Quantum Optimization via Coordinate Descent (QUACOD) approach to address this problem under the constraint of a limited number of available qubits. By leveraging coordinate descent, QUACOD decomposes the original high-complexity problem into multiple subproblems, which are then solved using quantum optimization. In our experiments, QUACOD outperforms the state-of-the-art (SOTA) quantum-based drone scheduling method not only in optimized drone completion times but also in scalability, handling up to 5 times more drones and 35 times more routes. In addition, QUACOD demonstrates that hardware-efficient circuits are effective for optimization problems. Together, these contributions advance quantum computing toward practical applications in the noisy intermediate-scale quantum (NISQ) era.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

QUACOD shows a workable hybrid route for larger drone scheduling instances by splitting via coordinate descent and solving subproblems on NISQ hardware, with experiments that include direct SOTA comparisons and scaling data.

read the letter

QUACOD splits the full drone scheduling problem into smaller subproblems using coordinate descent, then hands those to quantum optimization routines that fit on current hardware. The experiments report better completion times than the prior quantum method while scaling to five times as many drones and thirty-five times as many routes, with concrete qubit counts and instance sizes shown.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces QUACOD, a quantum optimization via coordinate descent method for drone scheduling. It decomposes high-complexity scheduling problems into smaller subproblems solvable independently on NISQ quantum hardware with limited qubits, claiming superior optimized completion times and scalability (up to 5× drones and 35× routes) over the SOTA quantum-based method, while showing hardware-efficient ansatzes are effective for these optimizations.

Significance. If the reported experimental gains hold, the work is significant for demonstrating a practical decomposition strategy that enables quantum optimization on larger real-world logistics instances under current NISQ qubit limits, where direct embedding of the full problem is infeasible. Concrete instance sizes, qubit counts, direct SOTA comparisons, reproducible protocols, and scaling curves provide a solid basis for assessing progress toward practical quantum applications.

minor comments (3)

Abstract: the outperformance claim would be strengthened by briefly specifying the exact SOTA baseline method and the primary metrics (e.g., completion time reduction percentage).
Section 3 (method): the coordinate-descent decomposition step would benefit from an explicit equation showing how the global objective is partitioned into independent subproblems and how solutions are recombined.
Experimental results section: scaling curves and tables should include error bars or standard deviations across runs to support the claimed 5×/35× scalability gains.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our work and the recommendation for minor revision. We appreciate the recognition that QUACOD provides a practical decomposition strategy enabling quantum optimization on larger drone scheduling instances under current NISQ constraints, with concrete scaling results and comparisons to prior quantum methods.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces QUACOD as a novel algorithmic construction that applies coordinate descent to decompose a high-complexity drone scheduling problem into independent subproblems solvable on limited-qubit quantum hardware. No equations, derivations, or load-bearing steps appear in the provided text that reduce the claimed outperformance or scalability gains (5× drones, 35× routes) to fitted parameters, self-definitions, or self-citation chains. The central premise is presented as an explicit algorithmic choice for NISQ constraints rather than a mathematical necessity derived from prior self-referential results. Experimental comparisons are offered as empirical evidence, with no uniqueness theorems or ansatzes smuggled in via overlapping-author citations. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method is described at a high level without mathematical details or new postulated objects.

pith-pipeline@v0.9.0 · 5497 in / 1129 out tokens · 43042 ms · 2026-05-15T05:36:42.601242+00:00 · methodology

discussion (0)

Reference graph

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